IDEAS home Printed from https://ideas.repec.org/p/zbw/iirmco/022018.html
   My bibliography  Save this paper

IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level

Author

Listed:
  • Scheller, Fabian
  • Johanning, Simon
  • Bruckner, Thomas

Abstract

Decision makers of municipal energy utilities responsible for future portfolio strategies are confronted with making informed decisions within the scope of continuously evolving systems. To cope with the increasing flexibility of customers, and their autonomous decision-making processes, determining newly established municipal energy-related infrastructure has become a challenge for utilities, which are struggling to develop suitable business models. Even though business portfolio decisions are already supported by energy system models, models only considering rational choices of economical drivers seem to be insufficient. Structural decisions of different market actors are often related to bounded rationality and thus are not fully rational. A combined analysis of sociological and technological dynamics might be necessary to evaluate new business models by providing insights into the interactions between the decision processes of market actors and the performance of the supply system. This research paper outlines a multi-model vision called IRPsim (Integrated Resource Planning and Simulation) including bounded and unbounded rationality modeling approaches. The techno-socio-economic model enables the determining of system impacts of behavior patterns of market actors on the business performance of the energy supply system. The mutual dependencies of the coupled models result in an interactive and dynamic energy model application for multi-year business portfolio assessment. The mixed-integer dynamic techno-economic optimization model IRPopt (Integrated Resource Planning and Optimization) represents an adequate starting point as a result of the novel actor-oriented multi-level framework. For the socioeconomic model IRPact (Integrated Resource Planning and Interaction), empirically grounded agent-based modeling turned out to be one of the most promising approaches as it allows for considering various influences on the adoption process on a micro level. Additionally, a large share of available applied research already deals with environmental and energy-related innovations.

Suggested Citation

  • Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
  • Handle: RePEc:zbw:iirmco:022018
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/183217/1/Beitrag-IIRM-2018-02.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beck, T. & Kondziella, H. & Huard, G. & Bruckner, T., 2017. "Optimal operation, configuration and sizing of generation and storage technologies for residential heat pump systems in the spotlight of self-consumption of photovoltaic electricity," Applied Energy, Elsevier, vol. 188(C), pages 604-619.
    2. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.
    3. Böttger, Diana & Götz, Mario & Theofilidi, Myrto & Bruckner, Thomas, 2015. "Control power provision with power-to-heat plants in systems with high shares of renewable energy sources – An illustrative analysis for Germany based on the use of electric boilers in district heatin," Energy, Elsevier, vol. 82(C), pages 157-167.
    4. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    5. Scheller, Fabian & Burgenmeister, Balthasar & Kondziella, Hendrik & Kühne, Stefan & Reichelt, David G. & Bruckner, Thomas, 2018. "Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach," Applied Energy, Elsevier, vol. 228(C), pages 2009-2023.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation," Ecological Economics, Elsevier, vol. 107(C), pages 411-421.
    8. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data: German market and policy options," Working Papers "Sustainability and Innovation" S12/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    9. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    10. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    11. Davide Natalini & Giangiacomo Bravo, 2013. "Encouraging Sustainable Transport Choices in American Households: Results from an Empirically Grounded Agent-Based Model," Sustainability, MDPI, vol. 6(1), pages 1-20, December.
    12. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    13. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data. Part I: Model structure and validation," Working Papers "Sustainability and Innovation" S4/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    14. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    15. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    16. Maya Sopha, Bertha & Klöckner, Christian A. & Hertwich, Edgar G., 2011. "Exploring policy options for a transition to sustainable heating system diffusion using an agent-based simulation," Energy Policy, Elsevier, vol. 39(5), pages 2722-2729, May.
    17. Stummer, Christian & Kiesling, Elmar & Günther, Markus & Vetschera, Rudolf, 2015. "Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 157-167.
    18. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    19. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
    20. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    21. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    22. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
    23. Koirala, Binod Prasad & Koliou, Elta & Friege, Jonas & Hakvoort, Rudi A. & Herder, Paulien M., 2016. "Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 722-744.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabian Scheller & Frauke Wiese & Jann Michael Weinand & Dominik Franjo Dominkovi'c & Russell McKenna, 2021. "An expert survey to assess the current status and future challenges of energy system analysis," Papers 2106.15518, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    2. Zhang, Cen & Schmöcker, Jan-Dirk & Kuwahara, Masahiro & Nakamura, Toshiyuki & Uno, Nobuhiro, 2020. "A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 135-150.
    3. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    4. Katarzyna Byrka & Arkadiusz Jedrzejewski & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Difficulty is critical: Psychological factors in modeling diffusion of green products and practices," HSC Research Reports HSC/15/10, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    5. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    6. Hidayatno, Akhmad & Jafino, Bramka Arga & Setiawan, Andri D. & Purwanto, Widodo Wahyu, 2020. "When and why does transition fail? A model-based identification of adoption barriers and policy vulnerabilities for transition to natural gas vehicles," Energy Policy, Elsevier, vol. 138(C).
    7. Byrka, Katarzyna & Jȩdrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna & Weron, Rafał, 2016. "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 723-735.
    8. Nugroho, Rizqi Ilma & Gnann, Till & Speth, Daniel & Purwanto, Widodo Wahyu & Hanafi, Jessica & Soehodho, Sutanto, 2024. "Agent-based simulation for market diffusion of passenger cars and motorcycles BEV in Greater Jakarta Area," Working Papers "Sustainability and Innovation" S05/2024, Fraunhofer Institute for Systems and Innovation Research (ISI).
    9. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    10. Yongchao Zeng & Peiwu Dong & Yingying Shi & Yang Li, 2018. "On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent-Based Model," Energies, MDPI, vol. 11(11), pages 1-21, November.
    11. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    12. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    13. Lixin Zhou & Jie Lin & Yanfeng Li & Zhenyu Zhang, 2020. "Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    14. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.
    15. Arias-Gaviria, Jessica & Larsen, Erik R. & Arango-Aramburo, Santiago, 2018. "Understanding the future of Seawater Air Conditioning in the Caribbean: A simulation approach," Utilities Policy, Elsevier, vol. 53(C), pages 73-83.
    16. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    17. Ensslen, Axel & Gnann, Till & Jochem, Patrick & Plötz, Patrick & Dütschke, Elisabeth & Fichtner, Wolf, 2020. "Can product service systems support electric vehicle adoption?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 343-359.
    18. Hesselink, Laurens X.W. & Chappin, Emile J.L., 2019. "Adoption of energy efficient technologies by households – Barriers, policies and agent-based modelling studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 29-41.
    19. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.
    20. Wolinetz, Michael & Axsen, Jonn, 2017. "How policy can build the plug-in electric vehicle market: Insights from the REspondent-based Preference And Constraints (REPAC) model," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 238-250.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:iirmco:022018. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iileide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.